Modelling impacts of water diversion on water quality in an urban artificial lake

Assessing petrochemical effluent effect on heavy metal pollution in Musa Estuary: A numerical modeling approach

The objective of this study is to assess the effect of petrochemical effluent on heavy metal pollutant in the Musa Estuary ecosystem in the North-western region of the Persian Gulf, through numerical modeling. The outfall of 30 petrochemical plants poses a potential threat to the estuary's seawater and sediment quality, environment, and public health. A combined hydrodynamic and ecologic modeling framework is applied to predict the spatial distribution of BOD and hazardous heavy metals in this estuary. MIKE 21 Flow Model (FM) CFD software is applied to simulate the tidal waves hydrodynamics, next to applying the MIKE ECO Lab models to predict the distribution of BOD and heavy metals in ambient water. The accuracy of the modeling framework is validated against measured water level, current speed, and water quality data. The results reveal that the level of lead concentration corresponds with the national standard, while the BOD, arsenic, molybdenum and vanadium exceed the limit in some areas, particularly in the tidal zone. The optimal outlet locations that effectively meet the standard concentrations of the heavy metals in the ambient water of the estuary are determined. The results confirm that the new outlet configuration corresponds with the standards: 0.198 μg/L for arsenic concentrations, 0.182 μg/L for molybdenum, 1.530 μg/L for vanadium, and 1.132 mg/L for BOD, at maximum. This study contributes to the perception of estuarine dynamics and provides practical implications for estuarine sustainable management and pollution control.

A continuation-dynamic constitution analysis approach based on digital stable marker tracing and study on simulation of ecological tidal water diversion

Water Diversion Projects have become increasingly popular in improving water quality in various water ecosystems. However, these projects also require a more comprehensive evaluation. In this study, we introduced a digital stable marker tracing module and proposed a continuation-dynamic constitution analysis approach. We applied this approach to analyze the ecological tidal water diversion in Changshu town, China. The results showed that the mean diversion water age of the Yangtze River water source was 10.80 h, the residence time of the background water source in Baimaotang was approximately 4.0 h, and the contribution of inflow water sources from tributaries accounted for 15% of discharges. The results can demonstrate practicality of our approach in quantitatively evaluating water diversion impacts and optimizing cooperative diversion projects. Furthermore, our discussion led to the design of an ecological tidal water diversion based on optimized cooperative diversion, which showed element-complementary and whole-comprehensive effects. This indicates that the ecological tidal water diversion can extend the impact of cooperative diversion. The continuation-dynamic constitution analysis approach enhances the tracing capacity of inflow constitution and enables the distinction of different time-varying distributions of each inflow constitution. Therefore, this approach holds promise as an embedded "Digital stable marker tracing" module in the model.

Water quality pollution assessment and source apportionment of lake wetlands: A case study of Xianghai Lake in the Northeast China Plain

Surface water pollution has always posed a serious challenge to water quality management. Improving water quality management requires figuring out how to comprehend water quality conditions scientifically and effectively as well as quantitatively identify regional pollution sources. In this study, Xianghai Lake, a typical lake-type wetland on the Northeast China Plain, was taken as the research area. Based on a geographic information system (GIS) method and 11 water quality parameters, the single-factor evaluation and comprehensive water quality index (WQI) methods were used to comprehensively evaluate the water quality of the lake-type wetland in the level period. Four key water quality parameters were determined by the principal component analysis (PCA) method, and more convenient comprehensive water quality evaluation models, the minimum WQI considering weights (WQImin-w) and the minimum WQI without considering weights (WQImin-nw) were established. The multiple statistical method and the absolute principal component score-multiple liner regression (APCS-MLR) model were combined to analyse the lake pollution sources based on the spatial changes in pollutants. The findings demonstrated that the WQImin-nw model's water quality evaluation outcome was more accurate when weights were not taken into account. The WQImin-nw model can be used as a simple and convenient way to comprehend the variations in water quality in wetlands of lakes and reservoirs. It was concluded that the comprehensive water quality in the study area was at a "medium" level, and CODMn was the main limiting factor. Nonpoint source pollution (such as agricultural planting and livestock breeding) was the most important factor affecting the water quality of Xianghai Lake (with a comprehensive contribution rate of 31.65%). The comprehensive contribution rates of sediment endogenous and geological sources, phytoplankton and other plants, and water diversion and other hydrodynamic impacts accounted for 25.12%, 19.65%, and 23.58% of the total impact, respectively. This study can provide a scientific method for water quality assessment and management of lake wetlands, and an effective support for migration of migratory birds, habitat protection and grain production security.

Water quality variation and driving factors quantitatively evaluation of urban lakes during quick socioeconomic development

Rapid urbanisation has caused a significant impact on the ecological environment of urban lakes in the world. To maintain the harmonious development of urban progress and water quality, it is essential to evaluate water quality variation and explore the driving factors quantitatively. A comprehensive evaluation method with cluster analysis and Kriging interpolation was used to explore the spatiotemporal variation in a typical urban lake in China, Chaohu Lake, from 2011 to 2020. The correlation between water quality and socioeconomic factors was evaluated by Pearson correlation analysis. Results indicated that: total phosphorus (TP) and total nitrogen (TN) were the key pollution parameters of Chaohu Lake. The pollution situation was gradually improving, however, and the improvement in chemical oxygen demand (COD) is more evident due to anthropogenic control. The spatial heterogeneity of water quality in Chaohu Lake is remarkable, and the water quality is poor in the west but better in the east. Natural attributes of lakes and external load were the main reasons for the spatial heterogeneity. The western residential areas of Chaohu Lake Basin (CLB) are concentrated, and a large amount of industrial and domestic sewage exacerbates water pollution in the west of tributaries. In contrast, the implementation of water environmental governance policies in recent years has alleviated water pollution. From 2011 to 2020, water quality has improved by 23%-35% in the west and 7%-14% in the east. This study provided a framework for quantitatively assessing water quality variation and its driving forces in urban lakes.

In Situ Remediation Technology for Heavy Metal Contaminated Sediment: A Review

Sediment is an important part of the aquatic ecosystem, which involves material storage and energy exchange. However, heavy metal pollution in sediment is on the increase, becoming an important concern for the world. In this paper, the state-of-art in situ remediation technology for contaminated sediment was elaborated, including water diversion, capping, electrokinetic remediation, chemical amendments, bioremediation and combined remediation. The mechanisms for these techniques to reduce/immobilize heavy metals include physical, electrical, chemical and biological processes. Furthermore, application principle, efficiency and scope, advantages and disadvantages, as well as the latest research progress for each restoration technology, are systematically reviewed. This information will benefit in selecting appropriate and effective remediation techniques for heavy metal-contaminated sediment in specific scenarios.

Challenges of Urban Artificial Landscape Water Bodies: Treatment Techniques and Restoration Strategies towards Ecosystem Services Enhancement

With the rapid adoption of green infrastructure and nature-based solutions for a low-impact development, much consideration is given to ecosystem services and the ecological enhancement in modern planning of urban spaces. Artificial landscape water bodies have, in recent years, been utilized to enhance the ecological quality of urban environments. As an environmentally friendly measure, the water source of these waters has predominantly been adopting reclaimed water (treated wastewater). As a result, landscape water bodies are often eutrophic, exhibiting poor hydrodynamics, with lengthy water change cycles, creating the ideal environment for algal blooms that negatively impact the aesthetic appeal of these landscape waters. Based on the existing literature, this paper summarizes the treatment techniques and strategies employed in enhancing the quality of urban artificial landscape water bodies and providing integrated design solutions in the urban environment.

Perfluoroalkyl acids (PFAAs) in the aquatic food web of a temperate urban lake in East China: Bioaccumulation, biomagnification, and probabilistic human health risk

The bioaccumulation and biomagnification of perfluoroalkyl acids (PFAAs) in temperate urban lacustrine ecosystems is poorly understood. We investigated the occurrence and trophic transfer of and probabilistic health risk from 15 PFAAs in the food web of Luoma Lake, a temperate urban lake in East China. The target PFAAs were widely distributed in the water (∑PFAA: 77.09 ± 9.07 ng/L), suspended particulate matter (SPM) (∑PFAA: 284.07 ± 118.05 ng/g dw), and sediment samples (∑PFAA: 67.77 ± 17.96 ng/g dw) and occurred in all biotic samples (∑PFAA: 443.27 ± 124.89 ng/g dw for aquatic plants; 294.99 ± 90.82 for aquatic animals). PFBA was predominant in water and SPM, with 40.11% and 21.35% of the total PFAAs, respectively, while PFOS was the most abundant in sediments (14.11% of the total PFAAs) and organisms (14.33% of the total PFAAs). Sediment exposure may be the major route of biological uptake of PFAAs. The PFAA accumulation capacity was the highest in submerged plants, followed by emergent plants > bivalves > crustaceans > fish > floating plants. Long-chain PFAAs were biomagnified, and short-chain PFAAs were biodiluted across the entire lacustrine food web. PFOS exhibited the greatest bioaccumulation and biomagnification potential among the target PFAAs. However, biomagnification of short-chain PFAAs was also observed within the low trophic-level part of the food web. Human health risk assessment indicated that perfluorooctanesulfonate (PFOS) and perfluorooctanoic acid (PFOA) posed health risks to all age groups, while the other PFAAs were unlikely to cause immediate harm to consumers in the region. This study fills a gap in the knowledge of the transfer of PFAAs in the food webs of temperate urban lakes.

A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory

Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality conditions has a significant social and economic value. In this study, a model based on an artificial neural network (ANN), discrete wavelet transform (DWT), and long short-term memory (LSTM) was constructed to predict the water quality of the Jinjiang River. Firstly, a multi-layer perceptron neural network was used to process the missing values based on the time series in the water quality dataset used in this research. Secondly, the Daubechies 5 (Db5) wavelet was used to divide the water quality data into low-frequency signals and high-frequency signals. Then, the signals were used as the input of LSTM, and LSTM was used for training, testing, and prediction. Finally, the prediction results were compared with the nonlinear auto regression (NAR) neural network model, the ANN-LSTM model, the ARIMA model, multi-layer perceptron neural networks, the LSTM model, and the CNN-LSTM model. The outcome indicated that the ANN-WT-LSTM model proposed in this study performed better than previous models in many evaluation indices. Therefore, the research methods of this study can provide technical support and practical reference for water quality monitoring and the management of the Jinjiang River and other basins.